The matrices \(\Phi _L\)  and \(\Phi _R\)  in the FlatCam imaging model are estimated based on the calibration procedure mentioned in [1]}. As we want to deal with RGB images, separate \(\Phi _L\)  and \(\Phi _R\)  matrices are calibrated for each of the R, G and B channels with the help of a Bayer color filter array on the sensor. We compare our results with OneNet and L2 regularisation, on two 256x256 images as shown in Figure REF . Our method shows better PSNR, SSIM, and perceptually better quality samples. Our method produces the least blurry solution and objects in the image has well defined boundaries. We use 25\(\%\)  pixel dropout and perform 1000 iterations of augmented Lagrangian method with the step size \(\alpha \)  as \(60.0\)  and \(\rho \)  as 10.
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